Modeling of Applications and Hardware to Explore Task Mapping and Scheduling Strategies on a Heterogeneous Micro-Server System. - Lilia Zaourar; Massinissa Ait Aba; David Briand; Jean-Marc Philippe (IPDPS Workshops, 2017)

PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks. - Alexandre Carbon; Jean-Marc Philippe; Olivier Bichler; Renaud Schmit; Benoît Tain; David Briand; Nicolas Ventroux; Michel Paindavoine; Olivier Brousse (DATE, 2018)

Task management on fully heterogeneous micro-server system: Modeling and resolution strategies. - Lilia Zaourar; Massinissa Ait Aba; David Briand; Jean-Marc Philippe (Concurr. Comput. Pract. Exp., 2018)

SamurAI: A 1.7MOPS-36GOPS Adaptive Versatile IoT Node with 15, 000× Peak-to-Idle Power Reduction, 207ns Wake-Up Time and 1.3TOPS/W ML Efficiency. - Ivan Miro Panades; Benoît Tain; Jean-Frédéric Christmann; David Coriat; Romain Lemaire; Clement Jany; Baudouin Martineau; Fabrice Chaix; Anthony Quelen; Emmanuel Pluchart; Jean-Philippe Noel; Reda Boumchedda; et al. (VLSI Circuits, 2020)

AdequateDL: Approximating Deep Learning Accelerators. - Olivier Sentieys; Silviu-Ioan Filip; David Briand; David Novo; Etienne Dupuis; Ian O'Connor; Alberto Bosio (DDECS, 2021)

Disentangled Loss for Low-Bit Quantization-Aware Training. - Thibault Allenet; David Briand; Olivier Bichler; Olivier Sentieys (CVPR Workshops, 2022)

Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization. - José Flich; Laura Medina; Izan Catalán; Carles Hernández 001; Andrea Bragagnolo; Fabrice Auzanneau; David Briand (ICIP, 2022)

SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration. - Ivan Miro-Panades; Benoît Tain; Jean-Frédéric Christmann; David Coriat; Romain Lemaire; Clement Jany; Baudouin Martineau; Fabrice Chaix; Guillaume Waltener; Emmanuel Pluchart; Jean-Philippe Noel; Adam Makosiej; et al. (IEEE J. Solid State Circuits, 2023)